
For pattern analysis, NLP could be useful for analysing corpora of examples for identifying patterns for repurposing. For example, if we collect a large corpus of cooking recipes, we can use text analysis tools to extract ingredient lists and, via statistical analysis, identify which ingredients are interchanged between different versions of the same, or similar, recipes. Also semi-supervised machine learning (ML) methods could be used to associate ingredients with classes (synsets) based on the usage of ingredients in recipes. The structure of a rich ontology can be extended and exploited in a number of ways. This includes representation learning approaches that embed each entity as a vector of numbers that capture its properties (e.g. node2vec [1]}), and have the potential to discover unnamed properties that help explain similarities of use, and thus support graph link prediction approaches to improve ontology coverage.

For property analysis, we can use ontologies to model information about the relevant entities of a domain, which may be derived from different sources and may be available in different formats, in a common way. For example, the literature on food science has good coverage of such classes (e.g. www.ifst.org). Recent developments in knowledge graphs can also be used for this purpose. For example, a recently developed knowledge graph, which contains structured (RDF) data about recipes and the nutritional value of ingredients is FoodKG (foodkg.github.io/). Some common substitutions that we can do in cooking can also be obtained from www.foodsubs.com/ (for example, www.foodsubs.com/Onionsdry.html has a list of substitutes for onions, which include garlic). For the recycling case study, a useful resource could be this: www.sciencedirect.com/science/article/pii/S2590238519303881. More generally, ML and automated reasoning technologies need to be harnessed. This includes the development of methods for acquisition of knowledge using ML techniques [2]}, [3]}, and for reasoning using knowledge-based technologies, such as description logic based systems for resource properties and features [4]}

For mechanistic analysis, we can draw on knowledge representation and reasoning. Commonsense reasoning is particularly important in intelligence (see Table REF ). There is a range of approaches in AI for such modelling including logic programming and event calculus [5]}. Commonsense reasoning is a core theme in AI [6]}, [7]}, [8]}, and will be critical in developing systems for supporting repurposing. Commonsense reasoning frameworks can be harnessed for contextualising repurposing situations [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [7]}.
Commonsense reasoning is particularly important for moving beyond repurposing derived from pattern matching methods to repurposing based on richer knowledge and automated reasoning, e.g. supporting mitigations and explanations.
So a key dimension to developing a general theory is to understand the types of commonsense reasoning and knowledge that arise in repurposing, and how these might be refined. E.g, in crisis management, there is often the need to substitute for basic human needs, but what is acceptable can vary between cultures. AI research in commonsense reasoning has not focused on culture differences, so the project may catalyse this study area.
Just look at how foods in one culture would not be acceptable as substitute even in extreme circumstances.

